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The cyclic matrix acts by $(v_1,v_2,v_3,v_4)\mapsto (v_2,v_3,v_4,v_1)$. For each fourth root of unity $\lambda$, the vector $v_{\lambda }=(1,\lambda ,{\lambda }^2,{\lambda }^3{)}^T$ satisfies $A{v}_{\lambda }=\lambda v_{\lambda }$, because ${\lambda }^4=1$. This supplies the four distinct eigenvalues and nonzero eigenvectors:Eigenvectors for distinct eigenvalues are linearly independent. Thus, with these columns in the indicated order,They form a basis over $\mathbb{C}$, so there are no other eigenvalues. Equivalently the characteristic polynomial is ${\lambda }^4-1$.

Use the standard complex inner product $⟨u,v⟩=u^{\ast }v$, conjugate-linear in its first argument. A unitary matrix satisfies $U^{\ast }U=I$, and a Hermitian matrix satisfies $H^{\ast }=H$, where $\ast$ is conjugate transpose.

If $Hv=\lambda v$ with $v\ne 0$, thenPositivity of $⟨v,v⟩$ proves $\boxed{\lambda \in \mathbb{R}}$. If $Uv=\lambda v$, preservation of the norm gives $\Vert v{\Vert }^2=\Vert Uv{\Vert }^2=|\lambda {|}^2\Vert v{\Vert }^2$, so $\boxed{|\lambda |=1}$.

For eigenvectors $Hv=\lambda v$ and $Hw=\mu w$, the Hermitian matrix identity gives $⟨v,Hw⟩=⟨Hv,w⟩$. The already established reality of the eigenvalues therefore implies $(\mu -\lambda )⟨v,w⟩=0$. Thus distinct eigenvalues have orthogonal eigenvectors.

Write the hyperbolic toral automorphism as $T_A([v])=[Av]$ on ${\mathbb{T}}^2={\mathbb{R}}^2/{\mathbb{Z}}^2$, where $A\in \mathrm{GL}(2,\mathbb{Z})$. Its determinant is $\pm 1$. The eigenvalues cannot be a nonreal conjugate pair: their product would have absolute value one, forcing each to have absolute value one, contrary to hyperbolicity. The same product condition and hyperbolicity imply two distinct real eigenvalues, labelled so thatWe will proveThis is the entropy of a hyperbolic toral automorphism; the absolute value is important when the expanding eigenvalue is negative.

Choose real eigenvectors $v_s,v_u$ and the adapted norm $\Vert a{v}_s+b{v}_u{\Vert }_{\ast }=\max (|a|,|b|)$. Its quotient metric on the torus isIt is a compatible metric, so part (a) allows its use. Let $\rho ={\min }_{0\ne \ell \in {\mathbb{Z}}^2}\Vert \ell {\Vert }_{\ast }>0$ and $D=\Vert A{\Vert }_{\ast }=|{\lambda }_u|$. Fix $ϵ>0$ small enough that $2ϵ<\rho$ and $(D+1)ϵ<\rho$.

If $[v]$ lies in the Bowen ball $B_n(0,ϵ)$, each $T_A^k[v]$ has a unique lift $z_k$ with $\Vert z_k{\Vert }_{\ast }<ϵ$. Uniqueness follows because two such lifts differ by a lattice vector of norm less than $2ϵ<\rho$. Moreover, $z_{k+1}-A{z}_k$ is a lattice vector, andIt must be zero. Thus $z_k=A^k{z}_0$: there is no hidden lattice jump between successive lifted iterates.

Write $z_0=a{v}_s+b{v}_u$. The simultaneous bounds $\Vert A^k{z}_0{\Vert }_{\ast }<ϵ$ for $0\le k<n$ are exactlyThe stable coordinate is largest at time zero and the unstable coordinate at time $n-1$. Conversely, every vector in this rectangle satisfies all the Bowen ball bounds. The quotient is injective on this rectangle by $2ϵ<\rho$. If $J=|\det (v_s,v_u)|$, its normalized Haar measure is thereforeThe quotient metric is translation invariant and $T_A$ is linear, so $d_n(x,y)=d_n(0,y-x)$; every Bowen ball has this same measure, independently of its centre.

Take an $ϵ$-separated set of largest cardinality $s_n(d,ϵ)$. It is maximal under inclusion, so its radius-$ϵ$ Bowen balls cover the torus: a point outside all these balls could be added. The covering bound givesIts radius-$ϵ/2$ Bowen balls are disjoint, by the triangle inequality for $d_n$. Their measures sum to at most one, givingTaking logarithms, dividing by $n$, and letting $n\to \infty$ yields $h_d(T_A,ϵ)=\log |{\lambda }_u|$ for every sufficiently small $ϵ$. The limit as $ϵ\downarrow 0$ proves the answer. Contraction in the stable direction contributes no exponential growth; only expansion in the unstable direction does.

Extend the selected ${\ell }_1$ to an orthonormal basis of eigenvectors, writing it as $v_1$. For a unit vector orthogonal to it, $a_1=0$, and thereforeA unit eigenvector $v_2$ attains equality. Thus ${\ell }_2$ is a unit eigenvector for the largest eigenvalue on ${\ell }_1^{\perp }$, and the maximum is ${\lambda }_2$. If the largest eigenvalue is repeated, a second orthogonal vector in that eigenspace is allowed and ${\lambda }_2={\lambda }_1$; there is no uniquely determined first pair of directions.

Let ${\lambda }_1\ge \cdots \ge {\lambda }_p\ge 0$ be the eigenvalues of the symmetric covariance matrix $V$, and choose an orthonormal basis of eigenvectors $v_1,\dots ,v_p$. If $\ell ={\sum }_j{a}_j{v}_j$ has unit length, then ${\sum }_j{a}_j^2=1$ andEquality is attained by $v_1$, and precisely by unit vectors in the top eigenspace. Hence choose ${\ell }_1$ to be any unit eigenvector for the largest eigenvalue; the maximum variance is ${\lambda }_1$. This proves the maximizing property of the Rayleigh quotient, rather than merely specifying a stationary point.

For the sample, put $\bar{x}=n^{-1}{\sum }_i{x}_i$ and use the sample covariance matrixOrder its eigenvalues ${\hat{\lambda }}_j$ decreasingly and choose unit orthogonal eigenvectors ${\hat{\ell }}_j$. The sample principal components are the score columnsTheir sample means are zero and their sample covariance is diagonal, since ${\hat{\ell }}_j^{T}S{\hat{\ell }}_k={\hat{\lambda }}_j{1}_{\{j=k\}}$. The $j$th sample variance is ${\hat{\lambda }}_j$, and its explained variance of a principal component is ${\hat{\lambda }}_j/\mathrm{tr}(S)$. Using divisor $n$ instead changes the numerical variances by a common factor but leaves the component directions unchanged.

Standardizing means replacing a variable with positive sample variance by $(x_{ij}-{\bar{x}}_j)/s_j$. This gives principal component analysis on a correlation matrix, using $C=D^{-1/2}S{D}^{-1/2}$ with $D=\mathrm{diag}(S)$. It removes arbitrary differences of measurement units and prevents a large-variance coordinate from dominating solely through its scale. It also changes the question being optimized: small-variance and possibly noisy coordinates receive equal marginal weight. Principal component analysis based on the covariance matrix can be more appropriate when the variables have common meaningful units and absolute variability matters. Standardization is therefore a substantive choice, not an automatic improvement. A zero-variance variable must first be removed. In the word-rating example the variables share the same numerical range, but their across-word dispersions can differ; standardization gives each attribute equal initial variance.

For the standardized eight-variable analysis, total variance is $\mathrm{tr}(C)=8$. The first three fractions of explained variance of a principal component are approximately 59.6%, 19.1% and 10.1%, respectively. The first two account for about 78.8%, and the first three for 88.9%. The first direction is dominant but one direction alone loses substantial variation. Retaining two principal components is a plausible descriptive summary: their eigenvalues exceed $1$, whereas the third is below $1$. That cutoff is a heuristic, and the third principal component may still have interpretable content.

The reported coefficient rows are not unit-length vectors. The printed rounded rows have squared lengths $4.7514$, $1.5222$ and $0.8063$, consistent with their eigenvalues to the precision of the coefficient table. They are consistent with the scaled principal component loading convention $r_j=\sqrt{{\lambda }_j}{\ell }_j$. Such rows remain eigenvectors, but the score direction with unit length is ${\ell }_j=r_j/\sqrt{{\lambda }_j}$, or more accurately the row divided by its actual Euclidean norm when using rounded entries. Under this convention $r_{ij}$ is the correlation of standardized attribute $i$ with the variance-one score for component $j$:This explains both their sizes and why they must not be treated as the unit vectors from parts (i) and (ii).

The first principal component has positive coefficients on every attribute, strongest on friendliness, goodness, niceness, bravery and strength. With the favorable endpoints coded in the positive direction, it describes a broad favorable or impressive evaluation, with a potency contribution. The second contrasts movement and speed, with some size and strength, against the first three evaluative attributes; it is chiefly an activity dimension. The third is dominated by size, with smaller positive strength and negative movement/speed coefficients, distinguishing size or potency from activity. These are interpretations of directions of variation, not evidence of causal latent traits. Reversing a principal component's overall sign changes neither its meaning as an axis nor its explained variance; endpoint coding fixes only how its positive side is described.

Direct multiplication of the gamma matrices in the chiral representation givesFor example ${\sigma }^1{\sigma }^2{\sigma }^3=i{I}_2$ by the Pauli matrix multiplication law, and the block multiplications give the displayed signs. Every ${\gamma }^{\mu }$ is off-diagonal in this representation, whereas the chirality matrix has opposite diagonal blocks. Therefore ${\gamma }^5{\gamma }^{\mu }+{\gamma }^{\mu }{\gamma }^5=0$ for all $\mu$.

The left-handed chirality condition makes the plane-wave coefficient $\lambda (p)=\left(\genfrac{}{}{0ex}{}{\chi }{0}\right)$. With $p\cdot x=E{x}^0-\mathbf{p}\cdot \mathbf{x}$, the Dirac equation givesSince $(\mathbf{\sigma }\cdot \mathbf{p}{)}^2=|\mathbf{p}{|}^2{I}_2$, its determinant is $E^2-|\mathbf{p}{|}^2$. A nonzero spinor therefore exists exactly whenFor nonzero momentum, put $r=|\mathbf{p}|$ and write its direction as $(\sin \vartheta \cos \phi ,\sin \vartheta \sin \phi ,\cos \vartheta )$. Normalized eigenvectors of $\mathbf{\sigma }\cdot \hat{\mathbf{p}}$ areThe general nonzero solution is $\lambda (p)=C\left(\genfrac{}{}{0ex}{}{{\chi }_{-}}{0}\right)$ for $E=r$ and $\lambda (p)=C\left(\genfrac{}{}{0ex}{}{{\chi }_{+}}{0}\right)$ for $E=-r$, with arbitrary nonzero complex normalization $C$. At $p=0$ any constant two-component $\chi$ solves the equation, but the momentum direction and helicity are undefined. For the physical positive-energy branch this is the left-handed massless plane-wave spinor.

A rotation by angle $\alpha$ about $\hat{\mathbf{p}}$ acts on the two-component spinor asThe plane-wave argument is unchanged because the rotation fixes $\mathbf{p}$, and $S(R){\chi }_{-}=e^{i\alpha /2}{\chi }_{-}$. Thus the positive-energy solution has helicity $h=-1/2$, where a helicity-$h$ state has rotation phase $e^{-i\alpha h}$. The negative-energy coefficient has the opposite spinor phase.

In a quantized complex left-handed Weyl field, the positive-frequency term annihilates particles and the negative-frequency term creates antiparticles. Reinterpreting negative energy also reverses its spatial momentum; for the positive physical momentum the two spinor coefficients have the same left-chiral kernel. Because one multiplies an annihilation operator and the other a creation operator, the corresponding particle and antiparticle state phases are conjugate. The helicity content of a quantized Weyl field is consequentlyThere is one particle spin state at each nonzero momentum, rather than the two helicities supplied by a full massless Dirac field. The right-handed Weyl field interchanges these signs. This is the massless relation between chirality and helicity; it does not identify them for a massive field.

The Schrödinger–Jaynes–Hughston–Jozsa–Wootters theorem classifies all finite quantum state ensembles of a density matrix. Write its nonzero spectral decomposition as $\rho ={\sum }_{a=1}^r{\lambda }_a|e_a⟩⟨e_a|$, with ${\lambda }_a>0$ and orthonormal eigenvectors. An ensemble with subnormalized vectors $|w_j⟩=\sqrt{p_j}|{\varphi }_j⟩$, $j=1,\dots ,m$, represents $\rho$ exactly whenHere $U$ is an $m\times r$ column linear isometry. Zero-weight vectors may be included or discarded. Equivalently, any two ensembles represent the same density matrix if and only if, after padding the shorter list with zero vectors, their subnormalized vectors are related by a unitary matrix acting on the ensemble index. This is the isometry parametrization of a density-matrix ensemble.

For necessity, if $|v⟩$ lies in the kernel of $\rho$, thenEvery $w_j$ therefore lies in the support of $\rho$. Define $U_{ja}=⟨e_a|w_j⟩/\sqrt{{\lambda }_a}$. The ensemble identity givesso these columns are orthonormal and the displayed parametrization follows.

Conversely, start with any such isometry and define $w_j$ by the boxed equation. ThenThe weights are $p_j=\Vert w_j{\Vert }^2={\sum }_a{\lambda }_a|U_{ja}{|}^2\ge 0$ and sum to $\mathrm{Tr}\rho =1$. Normalize every nonzero $w_j$ to obtain its pure state. This proves both directions and includes degenerate eigenvalues without any preferred choice of basis in their eigenspaces.

To prove the unitary-mixing version, pad two ensembles to a common length $L$. Their matrices $U$ and $V$ have $r$ orthonormal columns in ${\mathbb{C}}^L$. Extend each column family to an orthonormal basis, obtaining square unitaries $\tilde{U}$ and $\tilde{V}$. Then $W=\tilde{V}{\tilde{U}}^{†}$ is unitary and $V=WU$, so the second ensemble vectors satisfy $v_k={\sum }_j{W}_{kj}{w}_j$. Conversely this unitary mixing preserves ${\sum }_j|w_j⟩⟨w_j|$, because ${\sum }_k{W}_{ki}{W}_{kj}^{\ast }={\delta }_{ij}$. Independent phase choices for ensemble kets are absorbed in the row phases of these matrices.

The classification also has a measurement interpretation. Purify $\rho$ as $|\Psi ⟩={\sum }_a\sqrt{{\lambda }_a}|e_a⟩|a⟩$. On the ancillary system define $|{\eta }_j⟩={\sum }_a{U}_{ja}^{\ast }|a⟩$. The effects $E_j=|{\eta }_j⟩⟨{\eta }_j|$ sum to $I_r$, hence form a POVM. The conditional unnormalized system ket is $(I\otimes ⟨{\eta }_j|)|\Psi ⟩=w_j$, with probability $p_j$. Thus every ensemble can be realized by measuring a purifying system; with a sufficiently enlarged ancilla the measurement can be projective. The density matrix specifies the common statistics without selecting one particular ensemble decomposition.

On $a+2b=1$ the coexistence fixed point hasFor $1/9<a<1$ these give a nonreal conjugate pair on the unit circle. Nearby, while the pair remains complex, its modulus is $\sqrt{D}$; increasing $a+2b$ through one changes the modulus from greater than one to less than one. This is the candidate Neimark–Sacker bifurcation, and a nonlinear coefficient is needed to decide its direction.

Here that coefficient can be calculated explicitly. Let $\rho =e^{i\theta }$, $\sigma =\sin \theta >0$, and normalize the right eigenvector and complex projection byFor deviations $(X,Y)=zq+\overline{z}\overline{q}$, the quadratic derivative isThus $f_{20}=p\mathcal{B}(q,q)/2$, $f_{11}=p\mathcal{B}(q,\overline{q})$ and $f_{02}=p\mathcal{B}(\overline{q},\overline{q})/2$ are the quadratic coefficients in $z^{\prime }$. The quadratic coordinate change hasSubstitution into the map and collection of $w^2\overline{w}$ yieldsUsing $b=(1-a)/2$ and ${\sigma }^2=(1-a)(9a-1)/(16a^2)$ simplifies the cubic radial coefficient of a quadratic planar map toConsequently, away from strong resonances of a planar map, the bifurcation is supercritical: on $a+2b>1$ coexistence is attracting, whereas on $a+2b<1$ a small attracting invariant closed curve surrounds the unstable coexistence fixed point. If $\eta =\sqrt{D}-1>0$, its leading radius is $|w{|}^2=-\eta /\ell$, and its radial multiplier is $1-2\eta +\cdots$. Motion on the curve can be quasiperiodic or frequency-locked into periodic motion; stability of the curve alone does not imply an irrational rotation.

Two interior values need qualification: $a=1/7$ is a 1:3 resonance, and $a=1/5$ is a 1:4 resonance. At the former $h_{02}$ is singular and the quadratic ${\overline{w}}^2$ term must be retained. At the latter a cubic ${\overline{w}}^3$ term remains. The resonant terms can be displayed explicitly in the same normalization. At $a=1/7$, $f_{02}=-7/6-7\sqrt{3}i/2\ne 0$. The third iterate, near the resonance and after removing nonresonant quadratics, hasHere $\delta$ is the angular detuning. Its nonzero stationary solutions have $|w|=\sqrt{{\eta }^2+{\delta }^2}/|f_{02}|$ and three phases; they constitute a saddle three-cycle. Indeed the two leading real exponents at these solutions are $3[\eta \pm \sqrt{4{\eta }^2+3{\delta }^2}]$, of opposite signs. Therefore no ordinary attracting-circle conclusion follows arbitrarily close to the exact 1:3 point.

At $a=1/5$, the cubic resonant normal form isThe extra coefficient follows from $g_{03}=f_{11}{h}_{02}+2f_{02}{\overline{h}}_{20}$ and $h=\overline{\rho }{g}_{03}$. Its radial cubic damping is uniform since $\mathrm{Re}g+|h|<0$. The slow angular equation is $\Delta \arg w=\delta +|w{|}^2\mathrm{Im}(g+h{e}^{-4i\arg w})+\cdots$, so fourfold phase locking must be considered. For example, varying $b$ at fixed $a=1/5$ gives $\delta =-\eta /2+\cdots$. The locked branches satisfy $\tan (4\arg w)=6/17$ and $|w{|}^2=-\eta /\mathrm{Re}(g+h{e}^{-4i\arg w})$. For $\eta >0$ they form one attracting and one saddle four-cycle; the two branches alternate in phase. This is a resonant alternative to quasiperiodic motion on an invariant curve. The endpoints also fail the simple-complex-pair assumptions. This distinguishes the generic nearby dynamics from unjustified claims about every point on the printed interval.

Take the norm to be the Euclidean norm. The solution is $\mathbf{q}(t)=e^{tA}\mathbf{q}(0)$, so optimal growth is the operator norm $G(t)=\Vert e^{tA}{\Vert }_2$, equivalently the largest singular value of the matrix exponential. Let $s(A)$ be the spectral abscissa and let $w(A)={\lambda }_{\max }((A+A^{\ast })/2)$ be the numerical abscissa. The Euclidean semigroup growth bounds areFor the lower bound, the spectral radius of $e^{tA}$ is $e^{ts(A)}$ and is bounded by its operator norm. For the upper bound, differentiating $\Vert \mathbf{q}{\Vert }_2^2$ gives $2\mathrm{Re}({\mathbf{q}}^{\ast }A\mathbf{q})\le 2w(A)\Vert \mathbf{q}{\Vert }_2^2$, and integration gives the result. A non-normal matrix can have $w(A)>s(A)$, allowing transient amplification even when all its eigenvalues have negative real parts.

For the triangular matrix, $s(M)=-1/R$. The eigenvalues of its Hermitian part are $-3/(2R)\pm \frac{1}{2}\sqrt{1+R^{-2}}$, soThe upper exponent becomes positive when $R>\sqrt{8}$, even though the long-time spectrum is stable.

The two equations are $q_1^{\prime }=-q_1/R$ and $q_2^{\prime }=q_1-2q_2/R$. Solve the first, then use an integrating factor in the second. Writing $s=t/R$, $a=e^{-s}$, $b=e^{-2s}$ and $c=R(a-b)$ givesThe maximizing initial state is the unit eigenvector of $B^{\ast }B$ belonging to its largest eigenvalue. HereFor $t>0$, defineThe optimal initial state for triangular stable shear is thereforeMultiplication by any nonzero complex scalar gives the same growth ratio; at $t=0$, every nonzero initial state is optimal.

For $t\gg R$, $d\to \infty$ and $r\to 0$. Thus the optimal initial state tends to $(1,0{)}^T$, and $G(t)\sim e^{-t/R}\sqrt{1+R^2}$. The initial first component excites the slower eigenmode and its amplified second component.

For $t\ll R$, the exact expression gives $d=(R^{-1}+t/2)[1+O(t/R)]$. Accordingly the short-relative-time optimal state for triangular shear usesThis retains both the unequal damping rates and the shear. If $R\gg 1$, it reduces to $r\sim (\sqrt{t^2+4}-t)/2$, the maximizing direction for the shear matrix $\left(\begin{array}{cc}1& 0\\ t& 1\end{array}\right)$. If time also tends to zero at fixed $R$, it instead tends to $r=\sqrt{1+R^{-2}}-R^{-1}$, the largest-eigenvalue direction of the Hermitian part of $M$. Merely setting $t/R$ small does not justify setting $t$ small; the exact formula resolves both possibilities.

At fixed particle numbers, the perturbations satisfy $\int \delta n_i{d}^{3}x=0$. Expanding the uniform interaction energy, its linear term is $\int ({\mu }_1\delta n_1+{\mu }_2\delta n_2)d^{3}x=0$, and its exact quadratic change in this contact-interaction approximation isCompleting the square givesHence the miscibility criterion for a two-component condensate isThese are precisely strict positive definiteness of the interaction matrix, and make every nonzero admissible long-wavelength density perturbation cost energy. With both equilibrium densities positive, a negative eigenvalue would instead allow a small sinusoidal perturbation along its eigenvector, with zero mean and negative energy change. The omitted positive gradient term cannot cure arbitrarily long wavelengths in an unbounded system.

If cross-repulsion exceeds the geometric mean of the self-repulsions, the unstable direction favors separation of species; sufficiently strong cross-attraction favors a joint density increase and collapse. Equality gives a zero-cost direction at this order, so it is marginal rather than strictly stable. Constraints near an absent component or finite-system wavelength restrictions require a separate boundary analysis.

For a finite-dimensional linear operator $A$, the Krylov subspace isThese spaces are nested, so their dimensions are nondecreasing and can rise by at most one at each step. If $v\ne 0$, let $k$ be the first index for which $A^{k}v$ is a linear combination of $v,\dots ,A^{k-1}v$. The preceding $k$ vectors are independent. Multiplying that dependence by $A$ and repeatedly substituting shows every higher power also lies in their span. ThusSuch an index exists because the ambient space is finite-dimensional. If $v=0$, the separate answer is $d_m=0$ for every $m$, with $k=0$.

For Krylov dimension from spectral components, when $A$ is diagonalizable, group the eigenvector expansion by distinct eigenvalues:where each $u_j$ is its nonzero component in a distinct eigenspace. Then $A^{r}v={\sum }_j{\lambda }_j^r{u}_j$. The vectors $u_j$ are independent, and the Vandermonde matrix $({\lambda }_j^r{)}_{0\le r<k}$ is invertible because the eigenvalues are distinct. Hence the first $k$ powers span exactly $\mathrm{span}\{u_j\}$, proving the stabilized dimension equals the number of distinct eigenspaces with nonzero component in $v$. These $u_j$ themselves are eigenvectors, so this is also the minimum number of eigenvectors needed to represent $v$ when the eigenvectors may be chosen freely.

If the printed phrase instead means the count of nonzero coefficients in an arbitrary fixed eigenbasis, it is false with repeated eigenvalues. For example $A=I$ and $v=e_1+e_2$ use two vectors of that basis but have $\dim {\mathcal{K}}_m=1$; the vector $v$ itself is an eigenvector. For a simple spectrum there is no distinction. This qualification resolves the claim without assuming an unstated simple spectrum.